Contextual Feature Discovery for Mobile Devices
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Solution Overview
Problem
Users of mobile computing devices often remain unaware of features and applications that could be useful in specific contexts, leading to difficulty in discovering and utilizing them effectively.
Innovation Solution
A system and method that notifies users of mobile devices about features or applications relevant to their current context by determining environmental context using sensor data and historical user information, and provides tutorials to activate these features upon notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users are provided with comprehensive information about all device features, then user awareness of features improves, but information overload and user confusion increases
Solution Approach 1:
The patent applies local quality by providing feature information selectively based on contextual relevance rather than uniformly to all situations. The system analyzes current device state, user behavior patterns, and environmental context to determine which features should be highlighted, ensuring information is delivered where and when it is most needed without overwhelming the user with irrelevant details
Solution Approach 2:
The system performs preliminary analysis of user needs and device context before presenting feature information. By pre-processing contextual data and predicting which features will be relevant to the user's current situation, the system prepares and delivers targeted information in advance, preventing information overload while ensuring timely feature discovery
2Loss of information
If users are notified about every feature, then feature discovery improves, but user distraction and annoyance increases
Solution Approach 1:
The system performs preliminary filtering and prioritization of features based on contextual relevance before generating notifications. By analyzing user behavior patterns, device state, and environmental factors in advance, the system determines which features are worth notifying the user about, thereby reducing unnecessary notifications and minimizing distraction while maintaining effective feature discovery
Solution Approach 2:
The system incorporates feedback mechanisms to learn from user responses to notifications. By monitoring whether users engage with notified features or dismiss notifications, the system adjusts its notification strategy over time, reducing frequency and relevance of notifications that cause distraction while maintaining effectiveness for truly relevant features
3Measurement precision
If the system analyzes extensive user data and context, then notification accuracy improves, but processing time and energy consumption increases
Solution Approach 1:
The system applies partial action by selectively analyzing only the most relevant portions of user data and context for each notification opportunity rather than processing all available information. It prioritizes key contextual factors and uses heuristics to determine sufficient analysis depth, achieving acceptable notification accuracy while significantly reducing processing energy requirements compared to comprehensive analysis
Data Source
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AI summary
Certain implementations of the disclosed technology may include systems and methods for providing notifications relating to context-based features of a mobile device. According to an example implementation, a method is provided for receiving an indication of contextual information and an indication of historical information. The method also includes determining an environmental context of the mobile device from the contextual information and the historical information. The method also includes determining whether a usage criteria associated with a context-based feature associated with the environmental context has been met. The method also includes outputting an indication of the determination that the context-based feature has not met the usage criteria, such that the mobile device outputs a notification related to the context-based feature.